基于异构分布式系统的实时容错调度算法

目前文献中研究的实时容错调度算法都是基于同构分布式系统，系统中的所有处理机完全相同。该文首先建立了一个基于异构分布式系统实时容错调度模型，异构分布式系统中的各个处理机均不相同。基于该异构分布式系统模型，该文引入了可靠性代价（reliability cost）概念，并提出两种静态实时容错调度算法（RTFTNO和RTFTRC）用于调度周期性实时容错任务。算法RTFTRC在调度任务时，尽量使系统的可靠性代价最小；而算法RTFTNO在调度实时任务时，没有考虑系统的可靠性代价。该文详细讨论了两种调度算法的性能。性能模拟实验分别比较了两个算法的可靠性代价，超时比率和可调度性；并研究了任务的计算时间与可靠性代价的关系以及调度长度阈值与最小处理机个数的关系。实验结果表明，算法RTFTRC的性能优于算法RTFTNO。     

异构分布式系统中实时周期任务的容错调度算法

提出一个基于抢占性实时周期任务的可靠性调度模型,该模型与现有可靠性模型相比充分考虑了单处理机故障容错情况下的系统可靠性,因而更加接近现实和精确.在此基础上,提出一个基于异构分布式系统的实时容错调度算法IRDFTAHS,IRDFTAHS算法以提高系统的可靠性为目标来进行任务的分配,从而在不增加硬件代价的前提条件下通过调度增加了系统的可靠性.该算法同时支持主动和被动两种方式的副版本,使得容错调度算法具有更大的灵活性.最后,通过仿真实验对IRDFTAHS和现有的调度算法在几个方面进行比较.实验结果表明,IRDFTAHS算法的综合性能优于现有算法.     

实时多处理机系统BEST-FIT启发式容错调度

本文从有效利用资源的角度出发,提出了一种以最小化处理机数目为优化目标的Best-Fit启发式容错调度算法。该算法采用主/副版本备份技术和副版本的主动运行方式与被运行方式相结合的方法,将实时任务的主版本和副版本调度到不同处理机上运行;并且按照Best-Fit启发式策略为实时任务主版本寻找“最佳满足”处理机,使尽可能多的实时任务副版本以被动方式运行。算法既保证了系统的实时性和容错性,也节约了处理机。分析和仿真结果均证明了算法的有效性。     

异构分布式系统中基于负载均衡的容错调度算法

提出了基于主/从版本的具有容错功能的进程调度算法HDALF和HDLDF,且分别给出两种算法的时间复杂度并对算法的负载均衡性和节点资源利用率作了讨论.与以往容错调度算法不同的是,此算法是在被动进程复制模式下、适合于异构分布式系统的容错调度算法.而以往的研究都是建立在主从版本进程有相等的负载或执行时间相同的模型基础上,或者仅适合于同构分布式系统.实验结果表明,HDALF算法和HDLDF算法的性能比基于同构分布式模型下的两阶段算法更加优越.并且得出了这样的结果：当系统发生故障前后的负载均衡性权值相等时,在负载均衡和处理机资源利用率方面,HDLDF算法都要优于HDALF算法.     

异构分布式系统混合型实时容错调度算法

基/副版本技术是实现实时分布式系统容错的一个重要手段。提出了一种异构分布式混合型容错模型,该模型与传统的异构分布式实时调度模型相比同时考虑了周期和非周期调度任务。在此基础上给出3种容错调度算法:以可调度性为目的SSA算法、以可靠性为目的RSA算法、以负载均衡性为目的BSA算法。算法能够在异构系统中同时调度具有周期和非周期容错需求的实时任务,且能够保证在异构系统中某节点机失效情况下,实时任务仍然能在截止时间内完成。最后从可调度性、可靠性代价、负载均衡性、周期与非周期任务数及任务周期与粒度J个方面对算法进行了分析。模拟实验结果显示算法各有优缺点,所以在选择调度算法时应该根据异构系统的特点来选择。     

基于延迟主动副版本的分布式实时容错调度算法

主/副版本备份技术是分布式系统常用的实时容错调度方法,然而传统的主动方式副版本即使在无处理机故障时也需要在备份处理机上完全运行,增加了处理机消耗.提出一种基于固定优先级调度算法的延迟主动副版本(deferred active backup-copy)备份技术,该技术通过尽量向后调度主动方式的副版本,并在主版本成功执行时终止副版本的执行来减少备份的冗余度.在此基础上,提出一种基于该技术的以最小化处理机数目为优化目标的启发式任务分配算法--基于延迟主动副版本的最佳适应算法DABCBF(deferred active backup-copy based best-fit algorithm).DABCBF在保证系统的实时性和容错能力的前提条件下,通过尽量减少主版本的最坏响应时间来最大程度地减少冗余,以节省处理机.最后通过仿真实验,证明了算法的可行性和有效性.     

端到端时间约束的实时任务动态调度算法

在单处理机系统中,由于计算高优先级任务抢占的时间相对比较简单,所以单处理机调度理论取得了长足的进步.提出一个端到端时间约束的实时任务调度算法,当实时任务到达系统时,算法为任务的每个子任务在相应的处理机上预约一定的计算资源,把端到端的多处理机调度问题转换成单处理机调度问题,从而可以利用单处理机调度理论判定实时任务的可调度性.实验表明,该算法明显地提高了CPU利用率和任务接收率.     

容错多处理机中一种高效的实时调度算法

针对基于主副版本容错的多处理机中独立的、抢占性的硬实时任务,提出了一种高效的调度算法——TPFTRM(task partition based fault tolerant rate-monotonic)算法.该算法将单机实时RM 算法扩展到容错多处理机上,并且调度过程中从不使用主动执行的任务副版本,而仅使用被动执行和主副重叠方式执行的任务副版本,从而最大限度地利用副版本重叠和分离技术提高了算法调度性能.此外,TPFTRM 根据任务负载不同将任务集合划分成两个不相交的子集进行分配;还根据处理机调度的任务版本不同,将处理机集合划分成3 个不相交的子集进行调度,从而使TPFTRM 调度算法便于理解、实现以及减少了调度所需要的运行时间.模拟实验对各种具有不同周期和任务负载的任务集合进行了调度测试.实验结果表明,TPFTRM与目前所知同类算法相比,在调度相同参数的任务集合时不仅明显减少了调度所需要的处理机数目,还减少了调度所需要的运行时间,从而证实了TPFTRM 算法的高效性.     

基于多处理机的混合实时任务容错调度

提出了一种混合实时任务容错调度算法．该算法采用Rate Monotonic(RM)算法完成周期任务的静态调度；采用预订处理机时间方法和Earlier Deadline First(EDF)算法动态调度非周期任务；采用主／副版本备份技术确保系统的容错能力．通过充分利用周期任务的剩余处理机时间调度非周期任务和主动备份与被动备份相结合的方法有效地减少了处理机数．仿真结果证明了算法的有效性．     

异构环境中Fork-Join任务图的调度算法

目前已有的Fork-Join任务图的调度算法大多假定处理机为同构的,而没有考虑实际应用中处理机的异构性以及节省处理机的问题,导致算法在具体应用中效率较低.因此,对Fork-Join任务图的调度问题进行研究,提出了一个基于异构环境的贪心调度算法,该算法具有高的加速比和总体效率,其时间复杂度为O(v~2),其中,v表示任务集中任务的个数.实验结果表明,相比其它算法,该算法具有较短的调度长度、较短的完成时间,使用的处理机数较少,具有更强的实用性.     

基于校园网络的元计算实验系统WADE的设计与实现

元计算系统是可以作为虚拟的整体而使用的地理上分散的异构计算资源，这些资源包括计算机、数据库和昂贵仪器等．元计算系统在硬件和软件方面均有异构性，适合具有不同内在并行性的复杂应用的执行．现存的绝大多数并行系统都是同构的，不具有这一优势，因此，研究异构环境下的元计算系统就很有现实意义．WADE是基于校园网络开发的元计算实验系统，使用MD支持异构数据格式转换，使用面向对象技术实现单一映像系统，使用优先约束的任务调度算法来实现应用程序的调度和运行，并提供与流行的并行编程软件如PVM等的接口。     

High performance real-time scheduling of multiple mixed-criticality functions in heterogeneous distributed embedded systems

The architectures of high-end embedded system have evolved into heterogeneous distributed integrated architectures. The scheduling of multiple distributed mixed-criticality functions in heterogeneous distributed embedded systems is a considerable challenge because of the different requirements of systems and functions. Overall scheduling length (i.e., makespan) is the main concern in system performance, whereas deadlines represent the major timing constraints of functions. Most algorithms use the fairness policies to reduce the makespan in heterogeneous distributed systems. However, these fairness policies cannot meet the deadlines of most functions. Each function has different criticality levels (e.g., severity), and missing the deadlines of certain high-criticality functions may cause fatal injuries to people under this situation. This study first constructs related models for heterogeneous distributed embedded systems. Thereafter, the criticality certification, scheduling framework, and fairness of multiple heterogeneous earliest finish time (F_MHEFT) algorithm for heterogeneous distributed embedded systems are presented. Finally, this study proposes a novel algorithm called the deadline-span of multiple heterogeneous earliest finish time (D_MHEFT), which is a scheduling algorithm for multiple mixed-criticality functions. The F_MHEFT algorithm aims at improving the performance of systems, while the D_MHEFT algorithm tries to meet the deadlines of more high-criticality functions by sacrificing a certain performance. The experimental results demonstrate that the D_MHEFT algorithm can significantly reduce the deadline miss ratio (DMR) and keep satisfactory performance over existing methods.     

Performance evaluation of bag of gangs scheduling in a heterogeneous distributed system

Distributed systems deliver a cost-effective and scalable solution to the increasing performance intensive applications by utilizing several shared resources. Gang scheduling is considered to be an efficient time-space sharing scheduling algorithm for parallel and distributed systems. In this paper we examine the performance of scheduling strategies of jobs which are bags of independent gangs in a heterogeneous system. A simulation model is used to evaluate the performance of bag of gangs scheduling in the presence of high priority jobs implementing migrations. The simulation results reveal the significant role of the implemented migration scheme as a load balancing factor in a heterogeneous environment. Another significant aspect of implementing migrations presented in this paper is the reduction of the fragmentation caused in the schedule by gang scheduled jobs and the alleviation of the performance impact of the high priority jobs.     

基于禁忌算法的双层结构网格任务调度研究

合理的任务调度算法可以在很大程度上提高网格系统的利用率。针对网格环境异构、分布等特点,提出了基于禁忌搜索算法（TS）的双层结构网格任务调度算法,仿真实验表明,该方法具有良好的解质量和搜索能力,对异构系统中的任务调度具有较好的处理结果。     

Iterative list scheduling for heterogeneous computing

Optimal scheduling of parallel applications on distributed computing systems represented by directed acyclic graph (DAG) is NP-complete in the general case. List scheduling is a very popular heuristic method for DAG-based scheduling. However, it is more suited to homogenous distributed computing systems. This paper presents an iterative list scheduling algorithm to deal with scheduling on heterogeneous computing systems. The main idea in this iterative scheduling algorithm is to improve the quality of the schedule in an iterative manner using results from previous iterations. The algorithm first uses the heterogeneous earliest-finish-time (HEFT) algorithm to find an initial schedule and iteratively improves it. Hence the algorithm can potentially produce shorter schedule length. The simulation results show that in the majority of the cases, there is significant improvement to the initial schedule. The algorithm is also found to perform best when the tasks to processors ratio is large.     

Performance evaluation of a new scheduling algorithm for distributed systems with security heterogeneity

High quality of security service is increasingly critical for applications running on heterogeneous distributed systems. However, existing scheduling algorithms for heterogeneous distributed systems disregard security requirements of applications. To address this issue, in this paper, we introduce security heterogeneity concept for our scheduling model in the context of distributed systems. Based on the concept, we propose a novel heuristic scheduling algorithm, or SATS, which strives to maximize the probability that all tasks are executed without any risk of being attacked. Extensive experimental studies using real-world traces indicate that the scheduling performance is affected by heterogeneities of security and computational power. Additionally, empirical results demonstrate that with respect to security and performance, the proposed scheduling algorithm outperforms existing approaches under a wide spectrum of workload conditions.     

分布式系统下的DAG任务调度研究综述

近年来随着网格、云计算工作流等分布式计算技术的发展,关于DAG(有向无环图)模型任务在分布式系统环境下的调度问题逐渐成为备受关注的研究热点。根据最新研究进展,对分布式系统下的DAG任务调度问题和有关技术进行了研究与讨论,主要包括四个方面:系统地描述了分布式系统和异构分布式系统的有关概念,异构分布式系统下的DAG任务调度问题、调度模型及其典型应用;对现有分布式系统下DAG任务调度的研究按照不同的方式进行了分类;探讨了多DAG共享异构分布式资源调度的研究现状;讨论了目前多DAG共享异构分布式资源调度研究存在的问题和未来可能的研究方向。     

DAG Scheduling in Heterogeneous Computing and Grid Environments Using Variable Neighborhood Search Algorithm

DAG scheduling is a process that plans and supervises the execution of interdependent tasks on heterogeneous computing resources. Efficient task scheduling is one of the important factors to improve the performance of heterogeneous computing systems. In this paper, an investigation on implementing Variable Neighborhood Search (VNS) algorithm for scheduling dependent jobs on heterogeneous computing and grid environments is carried out. Hybrid Two PHase VNS (HTPHVNS) DAG scheduling algorithm has been proposed. The performance of the VNS and HTPHVNS algorithm has been evaluated with Genetic Algorithm and Heterogeneous Earliest Finish Time algorithm. Simulation results show that VNS and HTPHVNS algorithm generally perform better than other meta-heuristics methods.     

Cooperative scheduling mechanism for large-scale peer-to-peer computing systems

Over recent years, peer-to-peer (P2P) systems have become an important part of Internet. Millions of users have been attracted to their structures and services. P2P computing is a distributed computing paradigm that uses Internet to connect thousands, or even millions, of users into a single large virtual computer based on the sharing of computational resources. One of the most critical aspects to the design of P2P computing systems is the development of scheduling techniques to manage the computational resources efficiently and in a scalable way. This paper proposes a cooperative scheduling mechanism with a two-level topology designed to work on large-scale distributed computing P2P systems. Our main contribution is proposing three criteria that only use local information to schedule tasks thus providing scalability to the overall scheduling system. By setting up these three criteria, the system can be easily adapted to work efficiently with very different kinds of distributed applications. The extensive experimentation carried out justifies the importance of good scheduling in such heterogeneous systems, but also emphasizes the importance of having a scheduling algorithm capable of being adapted to the requirements of different kinds of application.